This project proposes a unified Internship Allocation and Recommendation System designed to streamline how students discover, apply for, and receive internship opportunities within academic institutions. The system integrates a web-based frontend and a Flask-powered backend with a structured SQLite database to automate key processes such as user authentication, eligibility validation, and recommendation generation. A hybrid recommendation mechanism combining rule-based logic, content-based filtering, and knowledge-based filtering is implemented to match students to internships based on skills, academic performance, and institutional criteria. The proposed architecture ensures secure role-based access for administrators and students, enabling efficient internship posting, candidate evaluation, and allocation management. Through modular components such as the Eligibility Validator, Recommendation Engine, and Application Manager, the system reduces manual coordination and enhances fairness, transparency, and consistency in internship allocation. Designed to be scalable and extensible, the platform serves as a foundational framework for future integration of machine learning models, multilingual support, analytics dashboards, and adaptive recommendation refinement.